Notes: * duplicated labels LK and 21LK; WS and WS cruise
Winter: Dec 20-March 19
Spring: March 20-June 18
Summer: June 19-Sep 20
Fall: Sep 21-Dec 19
Wet: May - October
Dry: November - April
#Get only FLK data#
Metadata<-read.csv("Data/FLKLocations.csv", header = T)
FLK.data<-join(CC.data, Metadata,
type = "left", by="SiteID")
str(FLK.data)
## 'data.frame': 1977 obs. of 84 variables:
## $ CTDID : chr "WS10067_1" "WS10067_2" "WS10067_3" "WS10067_4" ...
## $ Region : chr "FL" "FL" "FL" "FL" ...
## $ Year : num 2010 2010 2010 2010 2010 2010 2010 2010 2010 2010 ...
## $ Mission : chr "WaltonSmith" "WaltonSmith" "WaltonSmith" "WaltonSmith" ...
## $ Location : chr "Upper_Keys" "Upper_Keys" "Upper_Keys" "Upper_Keys" ...
## $ Latitude : num 25.6 25.6 25.1 25.1 25.1 ...
## $ Longitude : num -80.1 -80.1 -80.4 -80.4 -80.3 ...
## $ UTCDate : Date, format: "10-03-08" "10-03-08" ...
## $ UTCTime : chr "12:26:00 PM" "01:05:00 PM" "05:17:00 PM" "05:42:00 PM" ...
## $ Sample_Depth_m : num 0 0 0 0 0 0 0 0 0 0 ...
## $ DIC_umol_kg : num 2073 2068 2187 2112 2081 ...
## $ TA_umol_kg : num 2424 2418 2558 2446 2428 ...
## $ pH_measured : num NA NA NA NA NA NA NA NA NA NA ...
## $ pH_calculated : num 8.16 8.14 8.19 8.15 8.15 ...
## $ pCO2_uatm : num 302 317 293 313 314 ...
## $ Aragonite_Sat_W : num 3.83 3.83 4.07 3.62 3.79 ...
## $ Salinity_Bottle : num NA NA NA NA NA NA NA NA NA NA ...
## $ Conductivity_Sm : logi NA NA NA NA NA NA ...
## $ Salinity_CTD : num 36.5 36.5 36.7 36.7 36.6 ...
## $ Temperature_C : num 20.4 21.6 18.8 18.8 20.8 ...
## $ Pressure_db : num 0 0 0 0 0 0 0 0 0 0 ...
## $ Density_Sigmat : logi NA NA NA NA NA NA ...
## $ SiteID : Factor w/ 112 levels "","1","10","11",..: 2 29 37 46 47 68 69 80 83 85 ...
## $ Survey_design : chr "Permanent" "Permanent" "Permanent" "Permanent" ...
## $ Sample_frequency : chr "Single" "Single" "Single" "Single" ...
## $ ncrmp_flag : int 1 1 1 1 1 1 1 1 1 1 ...
## $ bottle_id : int 10021 10022 10023 10024 10025 10026 10027 10028 10029 10030 ...
## $ source_code : chr "E" "E" "E" "E" ...
## $ sample_num : int 1030 1031 1032 1033 1034 1035 1036 1037 1038 1039 ...
## $ bottle_name : chr "1" "2" "3" "4" ...
## $ deployment_id : int 517 517 517 517 517 517 517 517 517 517 ...
## $ project_code : chr "Walton Smith" "Walton Smith" "Walton Smith" "Walton Smith" ...
## $ collection_id : int 9221 9222 9223 9224 9225 9226 9227 9228 9229 9230 ...
## $ collected_date : chr "03/08/10" "03/08/10" "03/08/10" "03/08/10" ...
## $ collected_time : chr "12:26:00 PM" "01:05:00 PM" "05:17:00 PM" "05:42:00 PM" ...
## $ collected_timezone : chr "12:00:00 AM" "12:00:00 AM" "12:00:00 AM" "12:00:00 AM" ...
## $ density_id : int NA NA NA NA NA NA NA NA NA NA ...
## $ dic_id : int 17166 17167 17168 17169 17170 17171 17172 17173 17174 17175 ...
## $ calc_avg_dic : num 2086 2081 2200 2125 2094 ...
## $ dic_temp : num NA NA NA NA NA NA NA NA NA NA ...
## $ dic_machine : chr "SOMMA" "SOMMA" "SOMMA" "SOMMA" ...
## $ dic_kg_flag : int 1 1 1 1 1 1 1 1 1 1 ...
## $ calibration_dic_id : int 590 590 590 590 590 590 590 590 590 590 ...
## $ calc_avg_crm_init_dic : num 2014 2014 2014 2014 2014 ...
## $ crm_init_dic_temp : num NA NA NA NA NA NA NA NA NA NA ...
## $ calibration_dic_kg_flag: int 1 1 1 1 1 1 1 1 1 1 ...
## $ dic_crm_id : int 16 16 16 16 16 16 16 16 16 16 ...
## $ dic_crm_batch : int 85 85 85 85 85 85 85 85 85 85 ...
## $ dic_crm_dic : num 2000 2000 2000 2000 2000 ...
## $ dic_crm_salinity : num 33.3 33.3 33.3 33.3 33.3 ...
## $ ta_id : int 9096 9097 9098 9099 9100 9101 9102 9103 9104 9105 ...
## $ calc_avg_ta : num 2455 2450 2590 2477 2459 ...
## $ calc_avg_ta_temp : num NA NA NA NA NA NA NA NA NA NA ...
## $ ta_machine : chr "Langdonometer" "Langdonometer" "Langdonometer" "Langdonometer" ...
## $ ta_kg_flag : int 1 1 1 1 1 1 1 1 1 1 ...
## $ calibration_ta_id : int 626 626 626 626 626 626 626 626 626 626 ...
## $ calc_avg_crm_ta : num 2215 2215 2215 2215 2215 ...
## $ calc_avg_crm_ta_temp : num NA NA NA NA NA NA NA NA NA NA ...
## $ calibration_ta_kg_flag : int 1 1 1 1 1 1 1 1 1 1 ...
## $ ta_crm_id : int 16 16 16 16 16 16 16 16 16 16 ...
## $ ta_crm_batch : int 85 85 85 85 85 85 85 85 85 85 ...
## $ ta_crm_ta : num 2184 2184 2184 2184 2184 ...
## $ ta_crm_salinity : num 33.3 33.3 33.3 33.3 33.3 ...
## $ spec_ph_id : int NA NA NA NA NA NA NA NA NA NA ...
## $ spec_ph_temp : num NA NA NA NA NA NA NA NA NA NA ...
## $ rm_factor : num NA NA NA NA NA NA NA NA NA NA ...
## $ seacarb_id : int 4570 4571 4572 4573 4574 4575 4576 4577 4578 4579 ...
## $ UTCDate_Time : POSIXct, format: "2010-03-08 12:26:00" "2010-03-08 13:05:00" ...
## $ datetime : POSIXct, format: "2010-03-08 07:26:00" "2010-03-08 08:05:00" ...
## $ ESTDate : Date, format: "2010-03-08" "2010-03-08" ...
## $ ESTTime : chr "07:26:00" "08:05:00" "12:17:00" "12:42:00" ...
## $ Month : Factor w/ 12 levels "Apr","May","Jun",..: 12 12 12 12 12 12 12 12 12 12 ...
## $ Months : Factor w/ 12 levels "January","February",..: 3 3 3 3 3 3 3 3 3 3 ...
## $ MY : chr "2010-03" "2010-03" "2010-03" "2010-03" ...
## $ MoY : num 3 3 3 3 3 3 3 3 3 3 ...
## $ ToD : num 7.43 8.08 12.28 12.7 12.75 ...
## $ Season : chr "Winter" "Winter" "Winter" "Winter" ...
## $ Precipitation : chr "Dry" "Dry" "Dry" "Dry" ...
## $ dec.lon : num -80.1 -80.1 -80.4 -80.4 -80.3 ...
## $ dec.lat : num 25.6 25.6 25.1 25.1 25.1 ...
## $ Zone : chr "Inshore" "Offshore" "Inshore" "Inshore" ...
## $ Sub_region : chr "BB" "BB" "UK" "UK" ...
## $ Reference : chr "BB_1" "BB_1" "UK_3" "UK_3" ...
## $ Transect : chr "T1" "T1" "T1" "T1" ...
FLK.data<-subset(FLK.data, FLK.data$Sub_region!="NA")
FLK.data$Sub_region<-factor(FLK.data$Sub_region, levels = c(
"BB", "UK", "MK", "LK"))
FLK.data$Zone<-factor(FLK.data$Zone, levels = c(
"Inshore", "Mid channel",
"Offshore", "Oceanic"))
# Label and filter extreme events
FLK.data$Extreme<-"Normal"
FLK.data$Extreme[FLK.data$MY=="2010-03"] <-"ColdMortality"
FLK.data$Extreme[FLK.data$MY=="2010-08"] <-"Waves/Overcast"
FLK.data$Extreme[FLK.data$MY=="2011-10"] <-"LowSalinty"
FLK.data$Extreme[FLK.data$MY=="2011-08"] <-"HighOmega_NoReason"
FLK.data$Extreme[FLK.data$MY=="2019-07"] <-"Low_pH"
FLK.data$Extreme[FLK.data$MY=="2019-09"] <-"Low_pH"
FLK.data$Extreme[FLK.data$MY=="2019-11"] <-"Low_pH"
FLK.data$Extreme<-as.factor(FLK.data$Extreme)
Figure: Map of the sites where individual samples were collected
Notes: * Check location of individual SiteIDs
# Coordinates<-select(FLK.data, "CTDID", "Latitude", "Longitude", "SiteID")
# Coordinates<-join(Coordinates, WS.GPS.Sites,
# type = "left", by="SiteID")
# Coordinates$Lat_diff<-abs(Coordinates$Latitude-Coordinates$Lat)
# Coordinates$Lon_diff<-abs(Coordinates$Longitude-Coordinates$Lon)
# Check.coordinates1<- Coordinates[(Coordinates$Lat_diff>0.02), ]
# Check.coordinates2<- Coordinates[(Coordinates$Lon_diff>0.02), ]
# Check.coordinates<- rbind(Check.coordinates1, Check.coordinates2)
# Check.coordinates<-unique(Check.coordinates)
#write.csv(Check.coordinates, "FLK_results/2_CheckGPS_points_v3.csv")
Figure: Permanent location of WS stations
| Sub_region | Inshore | Mid channel | Offshore | Oceanic |
|---|---|---|---|---|
| BB | 84 | 76 | 83 | |
| UK | 107 | 71 | 111 | 37 |
| MK | 129 | 129 | 130 | 83 |
| LK | 138 | 138 | 171 | 87 |
| Sub_region | 2010 | 2011 | 2012 | 2014 | 2015 | 2016 | 2017 | 2018 | 2019 | 2020 | 2021 |
|---|---|---|---|---|---|---|---|---|---|---|---|
| BB | 4 | 14 | 3 | 6 | 30 | 36 | 30 | 35 | 24 | 24 | 37 |
| UK | 7 | 19 | 3 | 8 | 45 | 51 | 42 | 45 | 30 | 31 | 45 |
| MK | 15 | 51 | 9 | 11 | 55 | 66 | 55 | 56 | 44 | 44 | 65 |
| LK | 18 | 61 | 9 | 11 | 58 | 72 | 60 | 76 | 53 | 46 | 70 |
| Sub_region | Fall | Spring | Summer | Winter |
|---|---|---|---|---|
| LK | 149 | 133 | 129 | 123 |
| MK | 133 | 120 | 121 | 97 |
| UK | 88 | 82 | 84 | 72 |
| BB | 68 | 60 | 63 | 52 |
| 2010 | 2011 | 2012 | 2014 | 2015 | 2016 | 2017 | 2018 | 2019 | 2020 | 2021 | Months |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 38 | 38 | 19 | 6 | 35 | January | ||||||
| 16 | 22 | 38 | February | ||||||||
| 24 | 2 | 38 | 35 | 33 | March | ||||||
| 24 | 38 | 16 | 35 | 37 | April | ||||||
| 38 | 16 | 28 | May | ||||||||
| 20 | 24 | 38 | 38 | 35 | 8 | June | |||||
| 37 | 38 | 18 | 36 | 25 | July | ||||||
| 28 | 20 | 38 | 35 | 10 | August | ||||||
| 38 | 38 | 36 | September | ||||||||
| 28 | 38 | 18 | 38 | 36 | October | ||||||
| 37 | 35 | 38 | November | ||||||||
| 25 | 36 | 37 | 37 | 35 | December |
| Sub_region | Zone | Apr | May | Jun | Jul | Aug | Sep | Oct | Nov | Dec | Jan | Feb | Mar |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| BB | Inshore | 9 | 4 | 8 | 10 | 5 | 6 | 8 | 6 | 10 | 7 | 4 | 7 |
| BB | Mid channel | 9 | 4 | 5 | 10 | 5 | 6 | 7 | 6 | 8 | 7 | 3 | 6 |
| BB | Offshore | 9 | 4 | 8 | 10 | 5 | 6 | 8 | 6 | 9 | 7 | 4 | 7 |
| UK | Inshore | 13 | 6 | 8 | 13 | 8 | 7 | 9 | 9 | 11 | 10 | 4 | 9 |
| UK | Mid channel | 6 | 3 | 9 | 9 | 3 | 6 | 6 | 5 | 8 | 7 | 2 | 7 |
| UK | Offshore | 11 | 5 | 12 | 14 | 6 | 9 | 11 | 8 | 11 | 12 | 3 | 9 |
| UK | Oceanic | 3 | 1 | 5 | 4 | 2 | 3 | 4 | 2 | 4 | 3 | 2 | 4 |
| MK | Inshore | 12 | 8 | 15 | 13 | 11 | 9 | 13 | 9 | 14 | 10 | 6 | 9 |
| MK | Mid channel | 12 | 8 | 13 | 13 | 11 | 9 | 13 | 9 | 14 | 9 | 7 | 11 |
| MK | Offshore | 13 | 7 | 13 | 13 | 11 | 9 | 13 | 9 | 15 | 10 | 6 | 11 |
| MK | Oceanic | 7 | 4 | 8 | 8 | 8 | 6 | 8 | 6 | 10 | 6 | 5 | 7 |
| LK | Inshore | 12 | 7 | 16 | 9 | 15 | 9 | 15 | 9 | 15 | 11 | 8 | 12 |
| LK | Mid channel | 12 | 7 | 15 | 10 | 13 | 9 | 15 | 9 | 15 | 12 | 9 | 12 |
| LK | Offshore | 14 | 10 | 18 | 13 | 18 | 12 | 19 | 12 | 17 | 18 | 7 | 13 |
| LK | Oceanic | 8 | 4 | 10 | 5 | 10 | 6 | 9 | 5 | 9 | 7 | 6 | 8 |
| SiteID | 2010 | 2011 | 2012 | 2014 | 2015 | 2016 | 2017 | 2018 | 2019 | 2020 | 2021 |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | 2 | 6 | 1 | 1 | 5 | 6 | 5 | 6 | 4 | 4 | 6 |
| 10 | 2 | 5 | 1 | 1 | 5 | 6 | 5 | 5 | 4 | 4 | 5 |
| 11 | 2 | 4 | 1 | 1 | 5 | 6 | 5 | 5 | 4 | 4 | 6 |
| 12 | 1 | 5 | 1 | 1 | 5 | 6 | 5 | 6 | 4 | 4 | 6 |
| 13 | 1 | 4 | 1 | 1 | 5 | 6 | 5 | 4 | 4 | 4 | 6 |
| 14 | 1 | 3 | 1 | 1 | 5 | 6 | 5 | 5 | 4 | 4 | 6 |
| 16 | 2 | 6 | 1 | 1 | 5 | 6 | 5 | 6 | 4 | 4 | 5 |
| 17 | 2 | 6 | 1 | 1 | 5 | 6 | 5 | 7 | 4 | 4 | 5 |
| 18 | 2 | 6 | 1 | 1 | 5 | 6 | 5 | 7 | 4 | 4 | 6 |
| 19 | 2 | 6 | 1 | 5 | 6 | 5 | 6 | 4 | 4 | 6 | |
| 20 | 2 | 5 | 1 | 1 | 5 | 6 | 5 | 6 | 4 | 4 | 6 |
| 21.5 | 2 | 6 | 1 | 1 | 4 | 6 | 5 | 5 | 4 | 3 | 6 |
| 21LK | 1 | 5 | 1 | 5 | 6 | 5 | 5 | 4 | 4 | 6 | |
| 22 | 1 | 3 | 1 | 1 | 5 | 6 | 5 | 7 | 5 | 4 | 6 |
| 22.5 | 1 | 6 | 1 | 1 | 4 | 6 | 5 | 6 | 5 | 3 | 6 |
| 23 | 1 | 6 | 1 | 1 | 5 | 6 | 5 | 7 | 5 | 4 | 6 |
| 24 | 2 | 6 | 1 | 1 | 5 | 6 | 5 | 7 | 5 | 4 | 6 |
| 3 | 2 | 6 | 1 | 1 | 5 | 6 | 5 | 6 | 4 | 4 | 6 |
| 4 | 1 | 4 | 1 | 5 | 6 | 5 | 4 | 2 | 3 | 4 | |
| 5 | 1 | 4 | 1 | 1 | 5 | 6 | 5 | 4 | 2 | 3 | 4 |
| 5.5 | 2 | 2 | 1 | 1 | 5 | 5 | 4 | 4 | 3 | 3 | 4 |
| 6 | 2 | 4 | 1 | 5 | 5 | 4 | 4 | 3 | 3 | 5 | |
| 6.5 | 1 | 5 | 1 | 1 | 5 | 5 | 4 | 4 | 3 | 3 | 5 |
| 7 | 2 | 6 | 1 | 5 | 6 | 5 | 6 | 4 | 4 | 6 | |
| 8 | 2 | 5 | 1 | 1 | 5 | 6 | 5 | 6 | 4 | 4 | 6 |
| 9 | 2 | 6 | 1 | 5 | 6 | 5 | 6 | 4 | 4 | 6 | |
| 9.5 | 2 | 6 | 1 | 1 | 5 | 6 | 5 | 4 | 4 | 4 | 6 |
| 15 | 4 | 1 | 1 | 5 | 6 | 5 | 5 | 4 | 4 | 6 | |
| 15.5 | 3 | 1 | 1 | 5 | 6 | 5 | 4 | 4 | 4 | 6 | |
| 2 | 2 | 1 | 1 | 5 | 6 | 5 | 6 | 4 | 4 | 6 | |
| EK_IN | 1 | 5 | 6 | 5 | 6 | 4 | 4 | 7 | |||
| EK_MID | 1 | 5 | 6 | 5 | 5 | 4 | 4 | 6 | |||
| EK_OFF | 1 | 5 | 6 | 5 | 6 | 4 | 4 | 6 | |||
| UK_IN | 1 | 5 | 6 | 5 | 6 | 4 | 4 | 5 | |||
| UK_MID | 1 | 5 | 6 | 5 | 6 | 4 | 4 | 6 | |||
| UK_OFF | 1 | 5 | 6 | 5 | 6 | 4 | 4 | 6 | |||
| MR | 5 | 6 | 5 | 7 | 5 | 4 | 6 | ||||
| WS | 5 | 6 | 5 | 7 | 5 | 4 | 6 |
Use salinity Bottle first, if not available, salinity CTD
Calculate TA intercepts
# TA_coef <- FLK.data %>%
# group_by(Region) %>%
# do({model = lm(TA_umol_kg~BestSalinity, data=.) # model
# data.frame(tidy(model), # get coefficient info
# glance(model))}) # get model info
TA_coef <- FLK.data[FLK.data$Zone=="Oceanic",] %>%
group_by(Region) %>%
do({model = lm(TA_umol_kg~BestSalinity, data=.) # model
data.frame(tidy(model), # get coefficient info
glance(model))}) # get model info
DIC_coef <- FLK.data[FLK.data$Zone=="Oceanic",] %>%
group_by(Region) %>%
do({model = lm(DIC_umol_kg~BestSalinity, data=.)
# create model
data.frame(tidy(model), # get coefficient info
glance(model))}) # get model info
# DIC_coef <- FLK.data %>%
# group_by(Region) %>%
# do({model = lm(DIC_umol_kg~BestSalinity, data=.)
# # create model
# data.frame(tidy(model), # get coefficient info
# glance(model))}) # get model info
# 4. Normalize the data
TA_TO<-subset(TA_coef, term=="(Intercept)")
TA_TO<-as.data.frame(select(TA_TO, c(Region, estimate)))
names(TA_TO)[2] <- "TA_0"
DIC_TO<-subset(DIC_coef, term=="(Intercept)")
DIC_TO<-as.data.frame(select(DIC_TO, c(Region, estimate)))
names(DIC_TO)[2] <- "DIC_0"
Intercepts<-join(TA_TO, DIC_TO, by="Region", type="full")
FLK.data<-join(FLK.data, Intercepts, by="Region", type="left")
FLK.data$nTA<-(
(FLK.data$TA_umol_kg-FLK.data$TA_0)/
FLK.data$BestSalinity)*MeanSalinity+FLK.data$TA_0
FLK.data$nDIC<-(
(FLK.data$DIC_umol_kg-FLK.data$DIC_0)/
FLK.data$BestSalinity)*MeanSalinity+FLK.data$DIC_0
#write.csv(FLK.data, "FLK_results/FLK.data_normalized.csv", row.names = F)
kable(as.data.frame(TA_coef[,1:8], format = "markdown"),
caption = "TA parameters for normalization", digits = 3)
| Region | term | estimate | std.error | statistic | p.value | r.squared | adj.r.squared |
|---|---|---|---|---|---|---|---|
| FL | (Intercept) | 2111.560 | 57.288 | 36.859 | 0 | 0.099 | 0.094 |
| FL | BestSalinity | 7.515 | 1.592 | 4.719 | 0 | 0.099 | 0.094 |
#write.csv(TA_coef, "TA_coef.csv")
# 5. Chech normalization?
#nTA_Sal<- ggplot(FLK.data[FLK.data$Zone=="Oceanic",] ) + theme_bw() +
nTA_Sal<- ggplot(FLK.data) + MyTheme+
facet_grid(~Zone)+
geom_point(aes (BestSalinity, TA_umol_kg, fill=Season), alpha=0.5, shape=21)+
geom_smooth(aes(BestSalinity, TA_umol_kg), linetype=2, method = "lm", color="black")+
geom_point(aes (BestSalinity, nTA, fill=Season), alpha=0.5, shape=24)+
geom_smooth(aes(BestSalinity, nTA), linetype=1, method = "lm")
#geom_point(aes (BestSalinity, n35TA, fill=Season), alpha=0.5, shape=25)+
#geom_smooth(aes(BestSalinity, n35TA), linetype=3, method = "lm")+
#nTA_Sal
#ggsave(file="Outputs/Figure_1_Experiment_design.svg", plot=Design, dpi = 300, width=6, height=4)
kable(as.data.frame(DIC_coef[,1:8], format = "markdown"),
caption = " DIC parameters for normalization", digits = 3)
| Region | term | estimate | std.error | statistic | p.value | r.squared | adj.r.squared |
|---|---|---|---|---|---|---|---|
| FL | (Intercept) | 1786.677 | 115.049 | 15.530 | 0.000 | 0.025 | 0.02 |
| FL | BestSalinity | 7.244 | 3.199 | 2.264 | 0.025 | 0.025 | 0.02 |
#write.csv(DIC_coef, "DIC_coef.csv")
nDIC_Sal<- ggplot(FLK.data) + MyTheme+
facet_grid(~Zone)+
#nDIC_Sal<-ggplot(FLK.data[FLK.data$Zone=="Oceanic",] ) + theme_bw() +
geom_point(aes (BestSalinity, DIC_umol_kg, fill=factor(Season)),
alpha=0.5, shape=21)+
geom_smooth(aes(BestSalinity, DIC_umol_kg),
linetype=2, method = "lm", color="black")+
geom_point(aes (BestSalinity, nDIC, fill=factor(Season)), alpha=0.5, shape=24)+
geom_smooth(aes(BestSalinity, nDIC), linetype=1, method = "lm")
#nDIC_Sal
#ggsave(file="Outputs/Figure_1_Experiment_design.svg", plot=Design, dpi = 300, width=6, height=4)
FLK_parameters<-ggarrange(nTA_Sal, nDIC_Sal,
#labels = c("DIC Friss regresion", "TA Friss regresion"),
ncol = 1, nrow = 2)
FLK_parameters
Figure 1: Overview of linear regressions of total alkalinity (TA, µmol kg−1) and dissolved inorganic carbon (DIC, µmol kg−1) as a function of salinity + Friss normalized values.
All_ToD<- ggplot(FLK.data) +
theme_bw() +
scale_y_continuous(limits = c(0, 24),
expand = c(0.01, 0.01),
breaks = seq(0, 24, 2),
name="Time of the day")+
theme(legend.position="bottom",
plot.background=element_blank(),
panel.grid = element_blank(),
legend.box.background = element_rect(),
panel.background =element_rect(fill = NA,
color = "black")
)
nTA_dat<-All_ToD +geom_point(aes(nTA, ToD, colour=Sub_region))#+
#geom_smooth(aes(nDIC, ToD, colour=Sub_region), se=F)
ggMarginal(nTA_dat, groupColour = TRUE)
nDIC_dat<-All_ToD +geom_point(aes(nDIC, ToD, colour=Sub_region))#+
#geom_smooth(aes(nDIC, ToD, colour=Sub_region), se=F)
ggMarginal(nDIC_dat, groupColour = TRUE)
omega_dat<-All_ToD +geom_point(aes(Aragonite_Sat_W, ToD, colour=Sub_region))#+
#geom_smooth(aes(ToD, Aragonite_Sat_W, colour=Sub_region), se=F)
ggMarginal(omega_dat, groupColour = TRUE)
nTA_nDIC<- ggplot(FLK.data, aes (nDIC, nTA)) +
scale_y_continuous(#limits = c(1700,2900),
#expand = c(0, 0),
#breaks = seq(1800, 2800, 100),
name="nTA") +
scale_x_continuous(# limits = c(1600, 2600),
# expand = c(0, 0),
# breaks = seq(1500,2600,100),
name="nDIC")+
MyTheme
#TA_DIC+
#geom_point(aes (fill=Region, shape=Season), alpha=0.5)+
#scale_shape_manual(values=c(21, 22, 23, 24))
#ggsave(file="Outputs/Figure_1_Experiment_design.svg", plot=Design, dpi = 300, width=6, height=4)
nTA_nDIC_sea<-nTA_nDIC +
scale_shape_manual(values=c(21,21,24, 24))+
geom_point(aes (fill=Season, shape=Zone), alpha=0.4)+
geom_smooth(method = "lm", colour="black")
nTA_nDIC_pre<-nTA_nDIC +
scale_shape_manual(values=c(21,21,24, 24))+
geom_point(aes (fill=Precipitation, shape=Zone), alpha=0.4)+
geom_smooth(method = "lm", colour="black")
nTA_nDIC_parameters<-ggarrange(nTA_nDIC_sea, nTA_nDIC_pre,
#labels = c("DIC Friss regresion", "TA Friss regresion"),
ncol = 2, nrow = 1)
nTA_nDIC_parameters
Figure 2: Linear regressions of total alkalinity (nTA) as a function of disolved inorganic carbon (nDIC) for the Florida Keys .
Does this mean anything without offshore / inshore references?
# Individual LR for each region
TA_DIC_lm <- FLK.data %>%
group_by(Region) %>%
do(mod = lm(nTA ~ nDIC, data = .))
TA_DIC_coef <- FLK.data %>%
group_by(Region) %>%
do({model = lm(nTA~nDIC, data=.)
# create model
data.frame(tidy(model), # get coefficient info
glance(model))}) # get model info
kable(as.data.frame(TA_DIC_coef[,1:8], format = "markdown"),
caption = "nTA vs nDIC equations", digits = 3)
| Region | term | estimate | std.error | statistic | p.value | r.squared | adj.r.squared |
|---|---|---|---|---|---|---|---|
| FL | (Intercept) | 801.063 | 23.578 | 33.975 | 0 | 0.74 | 0.739 |
| FL | nDIC | 0.768 | 0.012 | 66.386 | 0 | 0.74 | 0.739 |
nTA_nDIC_sea_zone<-nTA_nDIC_sea + facet_grid(Zone~Sub_region)
nTA_nDIC_sea_zone
nTA_nDIC_pre_zone<-nTA_nDIC_pre + facet_grid(Zone~Sub_region)
nTA_nDIC_pre_zone
# Individual LR for each Zone and Sub_region
TA_DIC_lm_zone <- FLK.data %>%
group_by(Zone, Sub_region) %>%
do(mod = lm(nTA ~ nDIC, data = .))
TA_DIC_coef_zone <- FLK.data %>%
group_by(Zone, Sub_region) %>%
do({model = lm(nTA~nDIC, data=.)
# create model
data.frame(tidy(model), # get coefficient info
glance(model))}) # get model info
kable(as.data.frame(TA_DIC_coef_zone[,1:8], format = "markdown"),
caption = "nTA vs nDIC equations", digits = 3)
| Zone | Sub_region | term | estimate | std.error | statistic | p.value | r.squared |
|---|---|---|---|---|---|---|---|
| Inshore | BB | (Intercept) | 1059.443 | 82.755 | 12.802 | 0.000 | 0.755 |
| Inshore | BB | nDIC | 0.639 | 0.040 | 15.903 | 0.000 | 0.755 |
| Inshore | UK | (Intercept) | 1194.064 | 61.454 | 19.430 | 0.000 | 0.782 |
| Inshore | UK | nDIC | 0.581 | 0.030 | 19.326 | 0.000 | 0.782 |
| Inshore | MK | (Intercept) | 638.224 | 67.190 | 9.499 | 0.000 | 0.837 |
| Inshore | MK | nDIC | 0.849 | 0.033 | 25.456 | 0.000 | 0.837 |
| Inshore | LK | (Intercept) | 611.540 | 77.406 | 7.900 | 0.000 | 0.782 |
| Inshore | LK | nDIC | 0.845 | 0.039 | 21.819 | 0.000 | 0.782 |
| Mid channel | BB | (Intercept) | 1463.125 | 99.432 | 14.715 | 0.000 | 0.534 |
| Mid channel | BB | nDIC | 0.446 | 0.048 | 9.203 | 0.000 | 0.534 |
| Mid channel | UK | (Intercept) | 1710.133 | 105.684 | 16.182 | 0.000 | 0.369 |
| Mid channel | UK | nDIC | 0.328 | 0.052 | 6.351 | 0.000 | 0.369 |
| Mid channel | MK | (Intercept) | 776.598 | 86.379 | 8.991 | 0.000 | 0.728 |
| Mid channel | MK | nDIC | 0.782 | 0.043 | 18.353 | 0.000 | 0.728 |
| Mid channel | LK | (Intercept) | 652.923 | 79.371 | 8.226 | 0.000 | 0.774 |
| Mid channel | LK | nDIC | 0.837 | 0.039 | 21.448 | 0.000 | 0.774 |
| Offshore | BB | (Intercept) | 1468.342 | 140.296 | 10.466 | 0.000 | 0.343 |
| Offshore | BB | nDIC | 0.445 | 0.068 | 6.503 | 0.000 | 0.343 |
| Offshore | UK | (Intercept) | 1585.295 | 99.774 | 15.889 | 0.000 | 0.372 |
| Offshore | UK | nDIC | 0.388 | 0.049 | 7.963 | 0.000 | 0.372 |
| Offshore | MK | (Intercept) | 1101.649 | 99.094 | 11.117 | 0.000 | 0.565 |
| Offshore | MK | nDIC | 0.624 | 0.049 | 12.848 | 0.000 | 0.565 |
| Offshore | LK | (Intercept) | 823.857 | 91.742 | 8.980 | 0.000 | 0.636 |
| Offshore | LK | nDIC | 0.758 | 0.045 | 16.930 | 0.000 | 0.636 |
| Oceanic | UK | (Intercept) | 1947.839 | 176.508 | 11.035 | 0.000 | 0.151 |
| Oceanic | UK | nDIC | 0.213 | 0.086 | 2.462 | 0.019 | 0.151 |
| Oceanic | MK | (Intercept) | 2232.810 | 101.778 | 21.938 | 0.000 | 0.026 |
| Oceanic | MK | nDIC | 0.073 | 0.050 | 1.461 | 0.148 | 0.026 |
| Oceanic | LK | (Intercept) | 1900.529 | 122.199 | 15.553 | 0.000 | 0.158 |
| Oceanic | LK | nDIC | 0.235 | 0.060 | 3.945 | 0.000 | 0.158 |
Notes:
nTA_nDIC_Extreme<- ggplot() +
scale_y_continuous(#limits = c(1700,2900),
#expand = c(0, 0),
#breaks = seq(1800, 2800, 100),
name="nTA") +
scale_x_continuous(# limits = c(1600, 2600),
# expand = c(0, 0),
# breaks = seq(1500,2600,100),
name="nDIC")+
MyTheme + facet_grid(Zone~Sub_region)+
geom_point(data=FLK.data, aes (nDIC, nTA, fill=Extreme), shape=21, alpha=0.8)+
geom_smooth(data=FLK.data[FLK.data$Extreme=="Normal", ],
aes (nDIC, nTA), method = "lm", colour="black")
nTA_nDIC_Extreme
# Individual LR for each Zone and Sub_region
TA_DIC_coef_zone_noExtremes <- FLK.data[FLK.data$Extreme=="Normal", ] %>%
group_by(Zone, Sub_region) %>%
do({model = lm(nTA~nDIC, data=.)
# create model
data.frame(tidy(model), # get coefficient info
glance(model))}) # get model info
kable(as.data.frame(TA_DIC_coef_zone_noExtremes[,1:8], format = "markdown"),
caption = "nTA vs nDIC equations without extreme events", digits = 3)
| Zone | Sub_region | term | estimate | std.error | statistic | p.value | r.squared |
|---|---|---|---|---|---|---|---|
| Inshore | BB | (Intercept) | 989.867 | 84.382 | 11.731 | 0.000 | 0.786 |
| Inshore | BB | nDIC | 0.674 | 0.041 | 16.393 | 0.000 | 0.786 |
| Inshore | UK | (Intercept) | 1169.670 | 60.712 | 19.266 | 0.000 | 0.808 |
| Inshore | UK | nDIC | 0.593 | 0.030 | 19.921 | 0.000 | 0.808 |
| Inshore | MK | (Intercept) | 645.885 | 65.423 | 9.872 | 0.000 | 0.860 |
| Inshore | MK | nDIC | 0.846 | 0.033 | 25.974 | 0.000 | 0.860 |
| Inshore | LK | (Intercept) | 592.689 | 83.195 | 7.124 | 0.000 | 0.785 |
| Inshore | LK | nDIC | 0.855 | 0.042 | 20.487 | 0.000 | 0.785 |
| Mid channel | BB | (Intercept) | 1246.972 | 109.364 | 11.402 | 0.000 | 0.614 |
| Mid channel | BB | nDIC | 0.552 | 0.053 | 10.332 | 0.000 | 0.614 |
| Mid channel | UK | (Intercept) | 1553.603 | 124.023 | 12.527 | 0.000 | 0.418 |
| Mid channel | UK | nDIC | 0.405 | 0.061 | 6.667 | 0.000 | 0.418 |
| Mid channel | MK | (Intercept) | 698.676 | 75.025 | 9.313 | 0.000 | 0.817 |
| Mid channel | MK | nDIC | 0.822 | 0.037 | 22.169 | 0.000 | 0.817 |
| Mid channel | LK | (Intercept) | 735.616 | 78.104 | 9.418 | 0.000 | 0.785 |
| Mid channel | LK | nDIC | 0.797 | 0.038 | 20.698 | 0.000 | 0.785 |
| Offshore | BB | (Intercept) | 1160.019 | 172.795 | 6.713 | 0.000 | 0.409 |
| Offshore | BB | nDIC | 0.595 | 0.084 | 7.052 | 0.000 | 0.409 |
| Offshore | UK | (Intercept) | 1265.409 | 108.426 | 11.671 | 0.000 | 0.524 |
| Offshore | UK | nDIC | 0.545 | 0.053 | 10.270 | 0.000 | 0.524 |
| Offshore | MK | (Intercept) | 799.662 | 78.851 | 10.141 | 0.000 | 0.782 |
| Offshore | MK | nDIC | 0.772 | 0.039 | 19.958 | 0.000 | 0.782 |
| Offshore | LK | (Intercept) | 1223.653 | 85.134 | 14.373 | 0.000 | 0.559 |
| Offshore | LK | nDIC | 0.563 | 0.042 | 13.519 | 0.000 | 0.559 |
| Oceanic | UK | (Intercept) | 1541.985 | 200.853 | 7.677 | 0.000 | 0.375 |
| Oceanic | UK | nDIC | 0.411 | 0.098 | 4.172 | 0.000 | 0.375 |
| Oceanic | MK | (Intercept) | 2250.535 | 110.943 | 20.285 | 0.000 | 0.019 |
| Oceanic | MK | nDIC | 0.063 | 0.054 | 1.167 | 0.247 | 0.019 |
| Oceanic | LK | (Intercept) | 1560.316 | 128.211 | 12.170 | 0.000 | 0.365 |
| Oceanic | LK | nDIC | 0.401 | 0.063 | 6.393 | 0.000 | 0.365 |
Notes: * Offshore high nTA and nDIC during march 2012 cold mortality event - keep or remove?
FLK.data$Date<-FLK.data$ESTDate
| Season | Zone | Tmin | Tmax | Tmean | Tsd |
|---|---|---|---|---|---|
| Fall | Inshore | 20.01500 | 30.02900 | 25.59673 | 2.5421481 |
| Fall | Mid channel | 21.98100 | 29.85110 | 26.62821 | 2.0493446 |
| Fall | Offshore | 24.87700 | 29.97035 | 27.20738 | 1.5786848 |
| Fall | Oceanic | 24.90300 | 30.01320 | 27.39854 | 1.5239508 |
| Spring | Inshore | 22.23800 | 32.79000 | 27.85472 | 1.9082703 |
| Spring | Mid channel | 24.46990 | 31.26000 | 27.59277 | 1.6558646 |
| Spring | Offshore | 24.72433 | 31.12400 | 27.20822 | 1.4543694 |
| Spring | Oceanic | 25.07167 | 30.70000 | 27.28511 | 1.4048604 |
| Summer | Inshore | 26.85130 | 33.41420 | 30.94559 | 1.3603574 |
| Summer | Mid channel | 27.69640 | 33.54900 | 30.66481 | 1.0530289 |
| Summer | Offshore | 28.57000 | 32.47400 | 30.35070 | 0.7031212 |
| Summer | Oceanic | 29.36600 | 31.42030 | 30.27019 | 0.4979293 |
| Winter | Inshore | 17.50500 | 26.65400 | 22.66720 | 2.3468100 |
| Winter | Mid channel | 17.91567 | 26.79900 | 23.63902 | 1.8934145 |
| Winter | Offshore | 19.19967 | 27.21600 | 24.27852 | 1.5846746 |
| Winter | Oceanic | 21.34400 | 27.30000 | 24.58368 | 1.6286770 |
| Season | Zone | Sal_min | Sal_max | Sal_mean | Sal_sd |
|---|---|---|---|---|---|
| Fall | Inshore | 30.45233 | 38.11319 | 35.35753 | 1.2447767 |
| Fall | Mid channel | 31.88800 | 37.26015 | 35.62053 | 0.8406938 |
| Fall | Offshore | 33.47100 | 36.86900 | 35.71652 | 0.6347185 |
| Fall | Oceanic | 33.90255 | 37.18100 | 35.78349 | 0.5204920 |
| Spring | Inshore | 35.08521 | 37.73900 | 36.67620 | 0.5579413 |
| Spring | Mid channel | 35.45246 | 37.23920 | 36.44229 | 0.3454641 |
| Spring | Offshore | 34.82339 | 36.73600 | 36.33263 | 0.2553492 |
| Spring | Oceanic | 35.75627 | 36.66305 | 36.31844 | 0.2095783 |
| Summer | Inshore | 32.05500 | 41.03199 | 36.17870 | 1.1432207 |
| Summer | Mid channel | 33.94800 | 36.86176 | 35.86870 | 0.6988476 |
| Summer | Offshore | 33.84100 | 36.55435 | 35.72740 | 0.6229552 |
| Summer | Oceanic | 32.73250 | 36.51863 | 35.56385 | 0.7789012 |
| Winter | Inshore | 31.23138 | 38.32563 | 35.98450 | 0.9184624 |
| Winter | Mid channel | 33.25478 | 36.77829 | 36.12218 | 0.5298124 |
| Winter | Offshore | 33.64836 | 36.62865 | 36.20181 | 0.4361290 |
| Winter | Oceanic | 35.47867 | 36.56613 | 36.23997 | 0.2947979 |
Salinity_low<-FLK.data[FLK.data$BestSalinity<30, ]
Salinity_high<-FLK.data[FLK.data$BestSalinity>40, ]
Salinity_Check<-rbind(Salinity_low, Salinity_high)
Salinity_Check<-Salinity_Check[!is.na(Salinity_Check$BestSalinity),]
Salinity_Check$Problem<-"salinity"
Salinity_Check
| Season | Zone | Amin | Amax | Amean | Asd |
|---|---|---|---|---|---|
| Fall | Inshore | 2.265938 | 4.153585 | 3.237630 | 0.3377839 |
| Fall | Mid channel | 2.425071 | 4.194368 | 3.554209 | 0.2828936 |
| Fall | Offshore | 3.172859 | 4.472063 | 3.722611 | 0.2317087 |
| Fall | Oceanic | 3.261485 | 4.216995 | 3.779979 | 0.2274992 |
| Spring | Inshore | 2.804235 | 5.083392 | 4.139778 | 0.4495737 |
| Spring | Mid channel | 2.766048 | 4.711981 | 3.965309 | 0.3038914 |
| Spring | Offshore | 2.999385 | 4.251338 | 3.780495 | 0.1547279 |
| Spring | Oceanic | 1.998959 | 4.078478 | 3.764759 | 0.2894793 |
| Summer | Inshore | 1.835257 | 5.148959 | 3.621862 | 0.5640274 |
| Summer | Mid channel | 2.808160 | 4.918125 | 3.803552 | 0.3656937 |
| Summer | Offshore | 3.110143 | 5.133387 | 3.905004 | 0.3108872 |
| Summer | Oceanic | 3.553370 | 4.945832 | 4.016958 | 0.3165640 |
| Winter | Inshore | 2.401692 | 4.886632 | 3.626888 | 0.4247764 |
| Winter | Mid channel | 2.339249 | 4.649625 | 3.624081 | 0.3020022 |
| Winter | Offshore | 3.201501 | 4.312818 | 3.651986 | 0.1540584 |
| Winter | Oceanic | 2.491916 | 3.996918 | 3.676481 | 0.2192837 |
Omega_low<-FLK.data[FLK.data$Aragonite_Sat_W<2.1, ]
Omega_low2<-FLK.data[FLK.data$Aragonite_Sat_W<3 & FLK.data$Zone=="Oceanic", ]
Omega_high<-FLK.data[FLK.data$Extreme=="HighOmega_NoReason", ]
Omega_Check<-rbind(Omega_low, Omega_low2, Omega_high)
Omega_Check<-Omega_Check[!is.na(Omega_Check$Aragonite_Sat_W),]
Omega_Check$Problem<-"omega"
Omega_Check
| Season | Zone | TAmin | TAmax | TAmean | TAsd |
|---|---|---|---|---|---|
| Fall | Inshore | 2099.544 | 2609.396 | 2371.411 | 87.47261 |
| Fall | Mid channel | 2174.910 | 2479.733 | 2372.264 | 43.14992 |
| Fall | Offshore | 2245.810 | 2406.123 | 2376.256 | 20.65044 |
| Fall | Oceanic | 2353.810 | 2406.854 | 2379.771 | 10.49261 |
| Spring | Inshore | 2091.859 | 2398.144 | 2298.375 | 71.80582 |
| Spring | Mid channel | 2114.163 | 2406.498 | 2353.235 | 52.62884 |
| Spring | Offshore | 2262.590 | 2426.836 | 2376.541 | 21.89812 |
| Spring | Oceanic | 2339.600 | 2403.372 | 2382.003 | 12.16319 |
| Summer | Inshore | 1943.671 | 2494.752 | 2280.082 | 100.46661 |
| Summer | Mid channel | 2131.950 | 2411.730 | 2338.454 | 57.52611 |
| Summer | Offshore | 2293.290 | 2422.590 | 2369.555 | 25.84041 |
| Summer | Oceanic | 2343.852 | 2424.110 | 2382.153 | 16.92446 |
| Winter | Inshore | 2251.164 | 2781.489 | 2430.625 | 85.09654 |
| Winter | Mid channel | 2274.057 | 2678.780 | 2401.440 | 50.82598 |
| Winter | Offshore | 2352.434 | 2722.780 | 2389.688 | 36.50677 |
| Winter | Oceanic | 2354.310 | 2420.980 | 2383.905 | 15.82864 |
| Season | Zone | TAmin | TAmax | TAmean | TAsd |
|---|---|---|---|---|---|
| Fall | Inshore | 2100.062 | 2622.230 | 2375.332 | 88.87421 |
| Fall | Mid channel | 2183.076 | 2467.268 | 2374.259 | 41.58527 |
| Fall | Offshore | 2255.948 | 2426.871 | 2378.143 | 19.89908 |
| Fall | Oceanic | 2354.102 | 2413.657 | 2381.170 | 10.71712 |
| Spring | Inshore | 2092.177 | 2394.805 | 2295.050 | 71.14263 |
| Spring | Mid channel | 2114.175 | 2402.744 | 2350.306 | 52.12717 |
| Spring | Offshore | 2262.578 | 2426.413 | 2374.111 | 21.66102 |
| Spring | Oceanic | 2340.042 | 2399.080 | 2379.600 | 11.49296 |
| Summer | Inshore | 1945.264 | 2491.106 | 2280.584 | 101.13850 |
| Summer | Mid channel | 2131.472 | 2409.171 | 2339.601 | 58.30205 |
| Summer | Offshore | 2290.650 | 2423.974 | 2371.553 | 25.92975 |
| Summer | Oceanic | 2352.406 | 2430.996 | 2385.388 | 14.82703 |
| Winter | Inshore | 2251.407 | 2799.240 | 2431.781 | 89.97731 |
| Winter | Mid channel | 2287.465 | 2686.370 | 2400.489 | 51.34077 |
| Winter | Offshore | 2351.532 | 2719.498 | 2388.230 | 36.61700 |
| Winter | Oceanic | 2354.914 | 2416.243 | 2382.094 | 15.59514 |
TA_low<-FLK.data[FLK.data$nTA<2000, ] # One sample (WS20231_26) low TA no DIC
TA_high<-FLK.data[FLK.data$nTA>2780, ] # One sample (WS17030_16) high
TA_Check<-rbind(TA_low, TA_high)
TA_Check<-TA_Check[!is.na(TA_Check$nTA),]
TA_Check$Problem<-"TA"
TA_Check
| Season | Zone | DIC_min | DIC_max | DIC_mean | DIC_sd |
|---|---|---|---|---|---|
| Fall | Inshore | 1834.164 | 2347.652 | 2088.697 | 84.08436 |
| Fall | Mid channel | 1940.415 | 2188.637 | 2060.520 | 40.73712 |
| Fall | Offshore | 1956.640 | 2113.588 | 2049.048 | 29.11836 |
| Fall | Oceanic | 1957.930 | 2118.330 | 2047.986 | 29.49927 |
| Spring | Inshore | 1724.374 | 2078.210 | 1923.199 | 72.07535 |
| Spring | Mid channel | 1793.125 | 2109.220 | 2000.008 | 54.45072 |
| Spring | Offshore | 1918.379 | 2118.600 | 2042.020 | 24.54888 |
| Spring | Oceanic | 2003.835 | 2182.680 | 2049.730 | 24.77367 |
| Summer | Inshore | 1704.388 | 2186.988 | 1970.661 | 94.51700 |
| Summer | Mid channel | 1783.686 | 2159.260 | 2009.134 | 59.21836 |
| Summer | Offshore | 1925.061 | 2108.781 | 2032.781 | 32.27661 |
| Summer | Oceanic | 1979.375 | 2102.520 | 2036.585 | 28.68785 |
| Winter | Inshore | 1943.358 | 2429.507 | 2106.100 | 87.95805 |
| Winter | Mid channel | 1991.463 | 2310.755 | 2076.757 | 53.87955 |
| Winter | Offshore | 2012.429 | 2339.885 | 2062.173 | 33.69393 |
| Winter | Oceanic | 2016.510 | 2158.478 | 2054.552 | 23.70995 |
| Season | Zone | nDIC_min | nDIC_max | nDIC_mean | nDIC_sd |
|---|---|---|---|---|---|
| Fall | Inshore | 1832.114 | 2362.113 | 2094.227 | 85.92126 |
| Fall | Mid channel | 1955.011 | 2178.232 | 2063.105 | 39.13664 |
| Fall | Offshore | 1964.219 | 2115.344 | 2051.026 | 28.35117 |
| Fall | Oceanic | 1962.517 | 2130.570 | 2049.597 | 29.94949 |
| Spring | Inshore | 1723.884 | 2080.800 | 1921.052 | 71.80370 |
| Spring | Mid channel | 1793.154 | 2111.825 | 1997.540 | 54.32813 |
| Spring | Offshore | 1918.368 | 2117.514 | 2039.684 | 24.38300 |
| Spring | Oceanic | 2002.499 | 2183.448 | 2047.425 | 24.80804 |
| Summer | Inshore | 1710.799 | 2197.361 | 1971.612 | 96.78660 |
| Summer | Mid channel | 1783.699 | 2171.356 | 2010.607 | 61.57434 |
| Summer | Offshore | 1923.051 | 2114.592 | 2034.842 | 33.28861 |
| Summer | Oceanic | 1980.744 | 2103.706 | 2039.835 | 28.78964 |
| Winter | Inshore | 1938.599 | 2446.540 | 2107.336 | 92.33377 |
| Winter | Mid channel | 1988.309 | 2317.768 | 2076.023 | 55.15772 |
| Winter | Offshore | 2010.683 | 2336.915 | 2060.726 | 34.18601 |
| Winter | Oceanic | 2017.082 | 2158.698 | 2052.812 | 24.05237 |
Not filtering additional data becuase of nDIC
NOTE: Review Omega calculations for 2011 - Very high values for normal DIC and TA - including offshore and ocean samples
| Season | Zone | pH_min | pH_max | pH_mean | pH_sd |
|---|---|---|---|---|---|
| Fall | Inshore | 7.840293 | 8.109975 | 7.988004 | 0.0618658 |
| Fall | Mid channel | 7.908322 | 8.092337 | 8.019632 | 0.0406883 |
| Fall | Offshore | 7.937204 | 8.157007 | 8.034243 | 0.0379669 |
| Fall | Oceanic | 7.949192 | 8.104629 | 8.038466 | 0.0386797 |
| Spring | Inshore | 7.875770 | 8.271259 | 8.099446 | 0.0769675 |
| Spring | Mid channel | 7.877835 | 8.221791 | 8.066494 | 0.0547752 |
| Spring | Offshore | 7.942578 | 8.100547 | 8.040308 | 0.0286746 |
| Spring | Oceanic | 7.730269 | 8.092541 | 8.033934 | 0.0495861 |
| Summer | Inshore | 7.672562 | 8.172603 | 7.966685 | 0.0765092 |
| Summer | Mid channel | 7.868227 | 8.125938 | 7.990184 | 0.0457906 |
| Summer | Offshore | 7.883689 | 8.147040 | 8.004235 | 0.0413498 |
| Summer | Oceanic | 7.958271 | 8.119377 | 8.020132 | 0.0414338 |
| Winter | Inshore | 7.925370 | 8.246944 | 8.087370 | 0.0623184 |
| Winter | Mid channel | 7.920679 | 8.230011 | 8.076427 | 0.0439480 |
| Winter | Offshore | 8.033234 | 8.183797 | 8.073114 | 0.0242499 |
| Winter | Oceanic | 7.902464 | 8.144928 | 8.071353 | 0.0348490 |
pH_low<-FLK.data[FLK.data$pH_calculated<7.95 & FLK.data$Zone=="Oceanic", ]
pH_low$Problem<-"pH(low_for_ocean)"
NOTE: Review Omega calculations for August 2011 - Very high values for normal DIC and TA - including offshore and ocean samples
Remove<-rbind(Salinity_Check, Omega_Check, TA_Check, pH_low)
Remove <-Remove %>%
filter(!duplicated(CTDID))
#write.csv(Remove, "FLK_results/ValuesToRemove_V4.csv", row.names = F)
FLK.data_filtered<-FLK.data
#FLK.data_filtered<-FLK.data[!(FLK.data$CTDID %in% Remove$CTDID),]
FLK.data_filtered<-FLK.data_filtered%>% select (- (Survey_design: seacarb_id))
#str(FLK.data_filtered)
#write.csv(FLK.data_filtered, "FLK_results/FLK_filtered_ve3.csv", row.names = F)
local carbonate chemistry: Variation on CO2, alkalinity, and salinity - Florida Reef Tract (FRT) data.
Measures total CO2 (TCO2) - colometrically and total alkalinity (TA) - gran tritration. Other parameters derived
Predominantly in inshore / upper FRT (higher than modeled).
H Productivity -> net uptake of total CO2 (TCO2) -> H aragonite *Offshore reefs: oceanic carbonate chemistry consistent with present day tropical surface ocean conditions.
Photosynthetic uptake of TCO2 by seagrasses/macroalgae in the inshore waters of the FRT. Acidification refugia (spatial * time)